{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "........................................................................................................................................................................................................"
     ]
    }
   ],
   "source": [
    "using Flux, Flux.Data.MNIST, Statistics, Random, Plots\n",
    "using Flux: onehotbatch, onecold, crossentropy\n",
    "Random.seed!(0)\n",
    "\n",
    "epochs = 200\n",
    "eta = 1e-5\n",
    "batchSize = 10\n",
    "trainRange, validateRange = 1:1000, 1001:1500cour\n",
    "\n",
    "function minibatch(x, y, indexRange)\n",
    "    xBatch = Array{Float32}(undef, size(x[1])..., 1, length(indexRange))\n",
    "    for i in 1:length(indexRange)\n",
    "        xBatch[:, :, :, i] = Float32.(x[indexRange[i]])\n",
    "    end\n",
    "    return (xBatch, onehotbatch(y[indexRange], 0:9))\n",
    "end\n",
    "\n",
    "trainLabels = MNIST.labels()[trainRange]\n",
    "trainImgs = MNIST.images()[trainRange]\n",
    "mbIdxs = Iterators.partition(1:length(trainImgs), batchSize)\n",
    "trainSet = [minibatch(trainImgs, trainLabels, bi) for bi in mbIdxs]\n",
    "\n",
    "validateLabels = MNIST.labels()[validateRange]\n",
    "validateImgs = MNIST.images()[validateRange]\n",
    "validateSet = minibatch(validateImgs, validateLabels, 1:length(validateImgs))\n",
    "\n",
    "model = Chain(  flatten, Dense(784,400,relu),\n",
    "                Dense(400, 200, relu),\n",
    "                Dense(200, 100, relu), \n",
    "                Dense(100, 50, relu), \n",
    "                Dense(50, 10), softmax)\n",
    "\n",
    "opt = ADAM(eta)\n",
    "loss_path = []\n",
    "valid_error_path = []\n",
    "error_perf(x, y, model) = 1 - mean(onecold(model(x)) .== onecold(y))\n",
    "loss(x, y, model) = crossentropy(model(x), y)\n",
    "\n",
    "model(trainSet[1][1])\n",
    "for _ in 1:epochs\n",
    "    Flux.train!((x,y)->loss(x,y,model), params(model), trainSet, opt)\n",
    "    push!(valid_error_path, error_perf(validateSet..., model))\n",
    "    push!(loss_path, mean([loss(s...,model) for s in trainSet]))\n",
    "    print(\".\")\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "419860"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "number_of_params = 784*400 + 400 + 400*200 +200 + 200*100 +100 + 100*50 + 50 + 50*10 +10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "  \n",
       "  \"/>\n",
       "</svg>\n"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_err, best_epoch = findmin(valid_error_path)\n",
    "p1 = plot(valid_error_path,label = false,\n",
    "        ylim=(0.0,0.3), ylabel = \"Validation Error Rate\",lw=3)\n",
    "plot!([best_epoch,best_epoch],[0,best_err],c=:black,label=false,lw=4)\n",
    "annotate!(best_epoch+65, best_err-0.05, text(\"Early Stopping: epoch=$best_epoch\", :black, :right, 8))\n",
    "p2 = plot(loss_path,label = false,\n",
    "        ylim=(0.0,2), xlabel=\"Epoch\", ylabel = \"Training Loss\",lw=3)\n",
    "plot(p1,p2,layout=(2,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "savefig(\"~/Dropbox/MathEngDeepLearningBook/LaTeXBook/figures/chapter_4_figures/mnist_train_path.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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